This is a project conducted by the Howard Center for Investigative Reporting at Philip Merrill College of Journalism at the University of Maryland studying trends in homelessness. This markdown file analyzes data from the Vulnerability Index - Service Prioritization Decision Assistance Tool (VI-SPDAT) which can be found here
Data provided via email to Sean Mussenden, Data Editor for the Howard Center for Investigative Journalism, by Iain De Jong (idejong@orgcode.com) with OrgCode, the main individual who researched, designed and conducted the development testing for the VI-SPDAT.
##for debugging
rm(list=ls())
# install.packages("tidyverse")
# install.packages("stringr")
# install.packages("lubridate")
# install.packages("readxl")
# install.packages("janitor")
# install.packages("weathermetrics")
# install.packages("ggplot2")
# install.packages("reshape")
# install.packages("writexl")
# install.packages("purrr")
# install.packages("dplyr")
# install.packages("naniar")
library(tidyverse)
library(stringr)
library(lubridate)
library(readxl)
library(janitor)
library(weathermetrics)
library(ggplot2)
library(reshape)
library(writexl)
library(dplyr)
library(naniar)
rm(list=ls())
The SPDAT (Service Prioritization Decision Assistance Tool) was created by OrgCode Consulting, Inc. The survey is intended to be administered by a trained indidivual to assess a homeless individuals along four categories: Wellness, Risks, Socialization & Daily Functions, and History of Housing.
#here looks for where the project is located
#would need to install.packages("here")
load_path <- paste0(here::here(), "/data/input-data/")
#The data includes two sheets, one with data for unsheltered individuals and the other with data for sheltered individuals. These were kept separate in the analysis to study differences between respondents who reported mainly sleeping "unsheltered" compared to "sheltered"
unsheltered_raw <- read_xlsx(paste0(load_path, "vulnerability-survey.xlsx"))
sheltered_raw <- read_xlsx(paste0(load_path, "vulnerability-survey.xlsx"), sheet=2)
#clean the names of each column, words are now separated by _
#Separate date column into year, month and day columns
unsheltered_survey <- clean_names(unsheltered_raw) %>%
separate(date_of_assessment, sep = "-", into = c("year", "month", "day"), remove = F)
sheltered_survey <- clean_names(sheltered_raw) %>%
separate(date_of_assessment, sep = "-", into = c("year", "month", "day"), remove = F)
For year-over-year analysis, create a subset of the data for each year
unsheltered_2018 <- subset(unsheltered_survey, year==2018)
unsheltered_2017 <- subset(unsheltered_survey, year==2017)
unsheltered_2016 <- subset(unsheltered_survey, year==2016)
unsheltered_2015 <- subset(unsheltered_survey, year==2015)
sheltered_2018 <- subset(sheltered_survey, year==2018)
sheltered_2017 <- subset(sheltered_survey, year==2017)
sheltered_2016 <- subset(sheltered_survey, year==2016)
sheltered_2015 <- subset(sheltered_survey, year==2015)
CAVEAT: 2018 data is only complete through April 13 for sheltered and March 31 for unsheltered, while 2015 data doesn’t start until February 1 for unsheltered and April 1 for sheltered, so any analysis of those years will have lower-than-expected values
Age
#What is the average age of sheltered individuals who took this survey?
mean(sheltered_survey$age)
## [1] 45.82521
#Unsheltered
mean(unsheltered_survey$age)
## [1] 46.86456
Questions include:
#How many days (min, max and average) since an individual had permanent stable housing?
mean(sheltered_survey$how_long_has_it_been_since_you_lived_in_permanent_stable_housing_at_time_of_vi_spdat, na.rm=TRUE)
## [1] 410.414
max(sheltered_survey$how_long_has_it_been_since_you_lived_in_permanent_stable_housing_at_time_of_vi_spdat, na.rm=TRUE)
## [1] 14600
min(sheltered_survey$how_long_has_it_been_since_you_lived_in_permanent_stable_housing_at_time_of_vi_spdat, na.rm=TRUE)
## [1] 1
mean(unsheltered_survey$how_long_has_it_been_since_you_lived_in_permanent_stable_housing_at_time_of_vi_spdat)
## [1] 2632.762
max(unsheltered_survey$how_long_has_it_been_since_you_lived_in_permanent_stable_housing_at_time_of_vi_spdat, na.rm=TRUE)
## [1] 15000
min(unsheltered_survey$how_long_has_it_been_since_you_lived_in_permanent_stable_housing_at_time_of_vi_spdat, na.rm=TRUE)
## [1] 6
#Create a histogram to show frequency of responses; most respondents have been homeless once or twice in the past three years
mean(unsheltered_survey$in_the_last_three_years_how_many_times_have_you_been_homeless)
## [1] 1.91027
max(unsheltered_survey$in_the_last_three_years_how_many_times_have_you_been_homeless)
## [1] 14
hist(unsheltered_survey$in_the_last_three_years_how_many_times_have_you_been_homeless)
#Create a histogram to show frequency of responses; most respondents have been homeless once or twice in the past three years
mean(sheltered_survey$in_the_last_three_years_how_many_times_have_you_been_homeless, na.rm=TRUE)
## [1] 1.09714
max(sheltered_survey$in_the_last_three_years_how_many_times_have_you_been_homeless, na.rm=TRUE)
## [1] 44
hist(sheltered_survey$in_the_last_three_years_how_many_times_have_you_been_homeless)
Questions include:
In the past six months, how many times have you…
#Created a histogram for each question to show frequency in addition to calculating the average
#Emergency Room
mean(sheltered_survey$er)
## [1] 4.122844
hist(sheltered_survey$er)
mean(unsheltered_survey$er)
## [1] 7.73973
hist(unsheltered_survey$er)
#Ambulance
mean(sheltered_survey$ambulance)
## [1] 1.315142
hist(sheltered_survey$ambulance)
mean(unsheltered_survey$ambulance)
## [1] 2.73583
hist(unsheltered_survey$ambulance)
#Crisis Service
mean(sheltered_survey$crisis)
## [1] 0.5685013
#Crisis Service
mean(unsheltered_survey$crisis)
## [1] 0.9651737
hist(unsheltered_survey$crisis)
#Police Interaction
mean(sheltered_survey$police)
## [1] 2.077975
hist(sheltered_survey$police)
mean(unsheltered_survey$police)
## [1] 20.6056
hist(unsheltered_survey$police)
#Jail or Prison
mean(sheltered_survey$jail_or_prison)
## [1] 0.416568
hist(sheltered_survey$jail_or_prison)
mean(unsheltered_survey$jail_or_prison)
## [1] 6.799266
hist(unsheltered_survey$jail_or_prison)
#Attacked or Beaten
count(sheltered_survey, have_you_been_attacked_or_beaten_up_since_you_ve_become_homeless)
count(unsheltered_survey, have_you_been_attacked_or_beaten_up_since_you_ve_become_homeless)
#Threateneded or tried to harm self/someone else
count(sheltered_survey, have_you_threatened_to_or_tried_to_harm_yourself_or_anyone_else_in_the_last_year)
count(unsheltered_survey, have_you_threatened_to_or_tried_to_harm_yourself_or_anyone_else_in_the_last_year)
#Legal Obstacles?
#There is one "yy" value which is an assumed typo and is included with the "y" count
count(sheltered_survey, do_you_have_any_legal_stuff_going_on_right_now_that_may_result_in_you_being_locked_up_having_to_pay_nes_or_that_make_it_more_dif_cult_to_rent_a_place_to_live)
count(unsheltered_survey, do_you_have_any_legal_stuff_going_on_right_now_that_may_result_in_you_being_locked_up_having_to_pay_nes_or_that_make_it_more_dif_cult_to_rent_a_place_to_live)
#Forced or Tricked?
count(sheltered_survey, does_anybody_force_or_trick_you_to_do_things_that_you_do_not_want_to_do)
count(unsheltered_survey, does_anybody_force_or_trick_you_to_do_things_that_you_do_not_want_to_do)
#Risky Behavior
count(sheltered_survey, do_you_ever_do_things_that_may_be_considered_to_be_risky_like_exchange_sex_for_money_food_drugs_or_a_place_to_stay_run_drugs_for_someone_have_unprotected_sex_with_someone_you_don_t_know_share_a_needle_or_anything_like_that)
count(unsheltered_survey, do_you_ever_do_things_that_may_be_considered_to_be_risky_like_exchange_sex_for_money_food_drugs_or_a_place_to_stay_run_drugs_for_someone_have_unprotected_sex_with_someone_you_don_t_know_share_a_needle_or_anything_like_that)
Questions:
#Physical Health Led to Leaving Housing?
count(sheltered_survey, have_you_ever_had_to_leave_an_apartment_shelter_program_or_other_place_you_were_staying_because_of_your_physical_health)
count(unsheltered_survey, have_you_ever_had_to_leave_an_apartment_shelter_program_or_other_place_you_were_staying_because_of_your_physical_health)
#Chronic Issues?
count(sheltered_survey, do_you_have_any_chronic_health_issues_with_your_liver_kidneys_stomach_lungs_or_heart)
count(unsheltered_survey, do_you_have_any_chronic_health_issues_with_your_liver_kidneys_stomach_lungs_or_heart)
#Interested in program assisting those with HIV or AIDS?
count(sheltered_survey, if_there_was_space_available_in_a_program_that_speci_cally_assists_people_that_live_with_hiv_or_aids_would_that_be_of_interest_to_you)
count(unsheltered_survey, if_there_was_space_available_in_a_program_that_speci_cally_assists_people_that_live_with_hiv_or_aids_would_that_be_of_interest_to_you)
#Physical Disabilities limiting housing?
count(sheltered_survey, do_you_have_any_physical_disabilities_that_would_limit_the_type_of_housing_you_could_access_or_would_make_it_hard_to_live_independently_because_you_d_need_help)
count(unsheltered_survey, do_you_have_any_physical_disabilities_that_would_limit_the_type_of_housing_you_could_access_or_would_make_it_hard_to_live_independently_because_you_d_need_help)
#Avoid medical help?
count(sheltered_survey, when_you_are_sick_or_not_feeling_well_do_you_avoid_getting_medical_help)
count(unsheltered_survey, when_you_are_sick_or_not_feeling_well_do_you_avoid_getting_medical_help)
#Pregnant?
count(sheltered_survey, are_you_currently_pregnant)
count(unsheltered_survey, are_you_currently_pregnant)
#Kicked out due to drinking/drug use?
count(sheltered_survey, has_your_drinking_or_drug_use_led_you_to_being_kicked_out_of_an_apartment_or_program_where_you_were_staying_in_the_past)
#There are three N values, which is an assumed typo for n, and as such the Ns are included in the n totals
count(unsheltered_survey, has_your_drinking_or_drug_use_led_you_to_being_kicked_out_of_an_apartment_or_program_where_you_were_staying_in_the_past)
#Will drinking or drug issues make it difficult to afford/keep housing?
count(sheltered_survey, will_drinking_or_drug_use_make_it_dif_cult_for_you_to_stay_housed_or_afford_your_housing)
#There are four N values, which is an assumed typo for n, and as such the Ns are included in the n totals
count(unsheltered_survey, will_drinking_or_drug_use_make_it_dif_cult_for_you_to_stay_housed_or_afford_your_housing)
Have you ever had trouble maintaining your housing, or been kicked out of an apartment, shelter program or other place you were staying, because of:
#Mental health issue?
count(sheltered_survey, a_mental_health_issue_or_concern)
count(unsheltered_survey, a_mental_health_issue_or_concern)
#Past Head Injury?
count(sheltered_survey, a_past_head_injury)
count(unsheltered_survey, a_past_head_injury)
#Learning Disability
count(sheltered_survey, a_learning_disability_developmental_disability_or_other_impairment)
count(unsheltered_survey, a_learning_disability_developmental_disability_or_other_impairment)
#Difficulty living independently due to mental health or brain issues?
count(sheltered_survey, do_you_have_any_mental_health_or_brain_issues_that_would_make_it_hard_for_you_to_live_independently_because_you_d_need_help)
count(unsheltered_survey, do_you_have_any_mental_health_or_brain_issues_that_would_make_it_hard_for_you_to_live_independently_because_you_d_need_help )
#Not taking medications?
count(sheltered_survey, are_there_any_medications_that_a_doctor_said_you_should_be_taking_that_for_whatever_reason_you_are_not_taking)
count(unsheltered_survey, are_there_any_medications_that_a_doctor_said_you_should_be_taking_that_for_whatever_reason_you_are_not_taking)
#Misuse of medications?
count(sheltered_survey, are_there_any_medications_like_painkillers_that_you_don_t_take_the_way_the_doctor_prescribed_or_where_you_sell_the_medication)
count(unsheltered_survey, are_there_any_medications_like_painkillers_that_you_don_t_take_the_way_the_doctor_prescribed_or_where_you_sell_the_medication)
#Homelessness caused by trauma?
count(sheltered_survey, yes_no_trauma_or_abuse)
count(unsheltered_survey, yes_no_trauma_or_abuse)
These questions and responses are incldued in the data set but not in the survey. Many provide important additional metrics and so were analysized.
mean(unsheltered_survey$nights_registered_in_shelter_in_last_24_months)
## [1] 38.64954
max(unsheltered_survey$nights_registered_in_shelter_in_last_24_months)
## [1] 526
count(unsheltered_survey, nights_registered_in_shelter_in_last_24_months)
Depending on an individual’s responses, the surveyor assessed whether the individual possibly suffered from tri-morbidity (problems with physical health, mental health, and substance abuse).
Questions:
#How many of the three issues that make up tri-morbidity?
count(sheltered_survey, sub_score_trimorbidity)
count(unsheltered_survey, sub_score_trimorbidity)
#How many respondents were marked as tri-morbid?
count(sheltered_survey, trimorbidity)
count(unsheltered_survey, trimorbidity)
What percentage of unsheltered individuals identified as LGBTQQI?
#Identify as LGBT+
count(sheltered_survey, identifies_as_lgbtqqi2)
count(unsheltered_survey, identifies_as_lgbtqqi2)
#Change all answers to lowercase to eliminate duplicates
sheltered_survey$race_ethnicity <- tolower(sheltered_survey$race_ethnicity)
#Count the number of responses
count(sheltered_survey, race_ethnicity)
#Change all answers to lowercase to eliminate duplicates
unsheltered_survey$race_ethnicity <- tolower(unsheltered_survey$race_ethnicity)
#Count the number of responses
count(unsheltered_survey, race_ethnicity)
Find race/ethnicity per geographic area
sheltered_survey %>%
group_by(geographic_area, race_ethnicity) %>%
count(race_ethnicity)
unsheltered_survey %>%
group_by(geographic_area, race_ethnicity) %>%
count(race_ethnicity)
Race/Ethinicity Per Geographic Area Chart
ggplot(sheltered_survey, aes(x=race_ethnicity)) +
geom_bar() +
facet_wrap(~geographic_area) +
ggtitle("Sheltered Count of Race/Ethnicity by Geographic Area")
ggplot(unsheltered_survey, aes(x=race_ethnicity)) +
geom_bar() +
facet_wrap(~geographic_area) +
ggtitle("Unsheltered Count of Race/Ethnicity by Geographic Area")
Create a table of days from assessment to exit for sheltered:
#30 values are negatives. Unsure if these negatives are due to error or if surveys were given retroactively, so they are excluded. To view them, eliminate the line filter(date_diff_integer > 0)
sheltered_difference <- sheltered_survey %>%
select(date_of_assessment, date_of_exit_from_homelessness) %>%
mutate(date_diff = as.Date(date_of_exit_from_homelessness, format="%Y/%m/%d")-
as.Date(date_of_assessment, format="%Y/%m/%d")) %>%
mutate(date_diff_integer = as.integer(date_diff)) %>%
filter(date_diff_integer > 0)
sheltered_difference
Create a table of days from assessment to exit for unsheltered:
#150 values are negatives. Unsure if these negatives are due to error or if surveys were given retroactively, so they are excluded. To view them, eliminate the line filter(date_diff_integer > 0)
unsheltered_difference <- unsheltered_survey %>%
select(date_of_assessment, date_of_exit_from_homelessness) %>%
mutate(date_diff = as.Date(date_of_exit_from_homelessness, format="%Y/%m/%d")-
as.Date(date_of_assessment, format="%Y/%m/%d")) %>%
mutate(date_diff_integer = as.integer(date_diff)) %>%
filter(date_diff_integer > 0)
unsheltered_difference
mean(sheltered_difference$date_diff_integer, na.rm=TRUE)
## [1] 119.4695
max(sheltered_difference$date_diff_integer, na.rm=TRUE)
## [1] 437
mean(unsheltered_difference$date_diff_integer, na.rm=TRUE)
## [1] 161.8024
max(unsheltered_difference$date_diff_integer, na.rm=TRUE)
## [1] 400
Did those who were rehoused stay in their first house upon exit from homelessness? * Sheltered + No: 6626, 17.06% + Yes: 21562, 55.51% + NA/Blank: 10658, 27.44% * Unsheltered + No: 6011, 23.21% + Yes: 12562, 48.5% + NA/Blank: 7327, 28.29%
#NA values appear for type_of_housing_exit values: deceased, incarcerated, pending and unknown
#Changed to lowercase to eliminate one "Y" value which is assumedly an error for "y" and thus included in the "y" count
sheltered_survey$still_housed_in_first_housing_exit <- tolower(sheltered_survey$still_housed_in_first_housing_exit)
count(sheltered_survey, still_housed_in_first_housing_exit)
count(unsheltered_survey, still_housed_in_first_housing_exit)
Did whether someone stayed in their first house depend on the type of rehousing? * Deceased + NA = 868, 3.35%
* Family
+ No = 84, .32%
+ Yes = 165, .64%
* HUD-VASH
+ No = 418, 1.61%
+ Yes = 561, 2.17%
* Incarcerated
+ Yes = 4, .02%
+ NA = 876, 3.38%
* Pending
+ NA = 4096, 15.82%
* Permanent Supportive Housing (PSH)
+ No = 3351, 12.94%
+ Yes = 8170, 31.54%
* Rapid Re-Housing (RRH) + No = 1166, 4.5%
+ Yes = 2595, 10.02%
* Self-resolve
+ No = 297, 1.15%
+ Yes = 493, 1.9%
* Supportive Service for Veteran Families (SSVF) + No = 695, 2.68% + Yes = 573, 2.21%
* Unknown
+ Yes = 1, .004%
+ NA = 1487, 5.74%
unsheltered_survey %>%
group_by(type_of_housing_exit) %>%
count(still_housed_in_first_housing_exit)
Have homeless indidivudals experienced higher rates of incarceration/interactions with the law in a certain year? Do any correspond with increases in homeless criminilization policies?
Unsheltered respondents on average spent almost a week incarcerated prior to filling out the survey. Sheltered respondents, however, reported an average of less than half a day of incarceration. That said, the greatest number of days incarcerated reported by a sheltered individual, 100, and even the second-greatest amount, 60, are much higher than unsheltered’s maximum of 40 days (there were 10 responses of 40 days). The overall total of days spent incarcerated each year is about 10 times greater each year for unsheltered respondents than sheltered.
Basic incarceration statistics:
#Sheltered
max(sheltered_survey$jail_or_prison)
## [1] 100
#Unsheltered
max(unsheltered_survey$jail_or_prison)
## [1] 40
#Sheltered
mean(sheltered_survey$jail_or_prison)
## [1] 0.416568
#Unsheltered
mean(unsheltered_survey$jail_or_prison)
## [1] 6.799266
Next two coding sections show cumulative number of days respondents spent incarcerated each year
Unsheltered incarcerated days: * 2015: 50821
* 2016: 57097
* 2017: 58121
* 2018: 10062
#Unsheltered respondents
unsheltered_survey %>%
group_by(year) %>%
summarise(sum(jail_or_prison))
Sheltered incarcerated days: * 2015: 3938
* 2016: 5705
* 2017: 4976
* 2018: 1563
#Sheltered respondents
sheltered_survey %>%
group_by(year) %>%
summarise(sum(jail_or_prison))
The code below creates a histogram showing the number of responses each year for a certain value (the value being #days incarcerated). While many respondents - both sheltered and unsheltered - have spent time in jail or prison, unsheltered respondents on average spent significantly longer periods of time incarcerated, as shown by greater counts at jail_or_prison values greater than 1.
#Unsheltered Criminalization
ggplot(unsheltered_survey, aes(x=jail_or_prison)) +
geom_histogram() +
facet_wrap(~year) +
ggtitle("Frequency of #Days Unsheltered Spent Incarcerated Per Year")
#ggsave(filename = "unsheltered-jail-prison-time.png",
# device = "png", path = paste0(here::here(), "/graphs/unsheltered"))
#Sheltered Criminalization
ggplot(sheltered_survey, aes(x=jail_or_prison)) +
geom_histogram() +
facet_wrap(~year) +
ggtitle("Frequency of #Days Sheltered Spent Incarcerated Per Year")
#ggsave(filename = "sheltered-jail-prison-time.png",
# device = "png", path = paste0(here::here(), "/graphs/sheltered"))
#Create a historgram showing frequency of acuity scores by year
ggplot(unsheltered_survey, aes(x=unsheltered_survey$final_acuity_score)) +
geom_histogram() +
facet_wrap(~year) +
ggtitle("Frequency of Unsheltered Acuity Scores Per Year")
#Create a historgram showing frequency of acuity scores by year
ggplot(sheltered_survey, aes(x=sheltered_survey$final_acuity_score)) +
geom_histogram() +
facet_wrap(~year) +
ggtitle("Frequency of Sheltered Acuity Scores Per Year")
#Overall mean acuity score
mean(unsheltered_survey$final_acuity_score)
## [1] 9.865521
#Average acuity score by year
unsheltered_survey %>%
group_by(year) %>%
summarise(mean(final_acuity_score))
#Overall mean acuity score
mean(sheltered_survey$final_acuity_score)
## [1] 4.677676
#Average acuity score by year
sheltered_survey %>%
group_by(year) %>%
summarise(mean(final_acuity_score))
The geographic spread of unsheltered respondents across all years: * Midwest: 1683 * Northeast: 1072
* Southeast: 4389
* Southwest: 5633
* West: 13123
unsheltered_survey %>%
group_by(geographic_area) %>%
summarise("count" = n())
Count of unheltered respondents from each geographic region per year:
unsheltered_survey %>%
group_by(geographic_area, year) %>%
summarise("count" = n())
Count of sheltered respondents from each geographic region per year:
sheltered_survey %>%
group_by(geographic_area, year) %>%
summarise("count" = n())
Histogram showing change in geographic origin of unsheltered respondents over time: * Unsheltered respondents overwhelmingly originate in the West * The fewest unsheltered respondents come from the Northeast * Data for 2016 and 2017, the two complete years, show little change in count values for each region
ggplot(unsheltered_survey, aes(x=geographic_area)) +
geom_bar() +
facet_wrap(~year) +
ggtitle("Number of Unsheltered Respondents By Geographic Area Per Year")
Histogram showing change in geographic origin of sheltered respondents over time: * The fewest sheltered respondents came from the Northeast, similar to unsheltered, but the Midwest is a much closer second-least * Again most listed the West as their geographic origin. Here however, the second greatest number of sheltered respondents came from the Southeast, not the Southwest as for unsheltered respondents * Numbers across 2016 and 2017 are again relatively similar
ggplot(sheltered_survey, aes(x=geographic_area)) +
geom_bar() +
facet_wrap(~year) +
ggtitle("Number of Sheltered Respondents By Geographic Area Per Year")
Unsheltered respondents from: * Rural areas: 1358 * Suburban areas: 1584 * Urban areas: 22958
unsheltered_survey %>%
group_by(urban_suburban_rural) %>%
summarise("count" = n())
Sheltered respondents from: * Rural areas: 3147 * Suburban areas: 4256 * Urban areas: 31443
sheltered_survey %>%
group_by(urban_suburban_rural) %>%
summarise("count" = n())
Unsheltered respondents from: * Midwest: + Rural areas: 120 + Suburban areas: 111 + Urban areas: 1452 * Northeast: + Rural areas: 46 + Suburban areas:59 + Urban areas: 967 * Southeast: + Rural areas: 208 + Suburban areas: 242 + Urban areas: 3939 * Southwest: + Rural areas: 280 + Suburban areas: 334 + Urban areas: 5019 * West: + Rural areas: 704 + Suburban areas: 838 + Urban areas: 11581
unsheltered_survey %>%
group_by(geographic_area, urban_suburban_rural) %>%
summarise("count" = n())
ggplot(unsheltered_survey, aes(x=urban_suburban_rural)) +
geom_bar() +
facet_wrap(~geographic_area) +
ggtitle("Unsheltered Rural/Suburban/Urban Numbers Per Geographic Area")
Sheltered respondents from: * Midwest: + Rural areas: 448 + Suburban areas: 584 + Urban areas: 4393 * Northeast: + Rural areas: 377 + Suburban areas: 570 + Urban areas: 4023 * Southeast: + Rural areas: 652 + Suburban areas: 931 + Urban areas: 6489 * Southwest: + Rural areas: 596 + Suburban areas: 793 + Urban areas: 6207 * West: + Rural areas: 1074 + Suburban areas: 1378 + Urban areas: 10331
sheltered_survey %>%
group_by(geographic_area, urban_suburban_rural) %>%
summarise("count" = n())
ggplot(sheltered_survey, aes(x=urban_suburban_rural)) +
geom_bar() +
facet_wrap(~geographic_area) +
ggtitle("Sheltered Rural/Suburban/Urban Numbers Per Geographic Area")
#Unsheltered data
ggplot(unsheltered_survey, aes(x=year)) +
geom_bar() +
facet_wrap(~urban_suburban_rural) +
ggtitle("Unsheltered Rurual/Suburban/Urban Numbers per Year")
#Sheltered data
ggplot(sheltered_survey, aes(x=year)) +
geom_bar() +
facet_wrap(~urban_suburban_rural) +
ggtitle("Sheltered Rurual/Suburban/Urban Numbers per Year")
Socialization and Daily Functioning
Questions: